Adventures in Machine Learning

Mastering Techniques for Updating Python Dataframe Rows

Updating row values in a Python Dataframe

Dataframes are a fundamental data structure in the Pandas library that make it easy to work with tabular data. They are similar to a spreadsheet, with rows and columns that can be manipulated and analyzed in various ways using Python.

In this article, we will explore various methods of updating row values in a Python Dataframe.

Creating a Dataframe

Before we can update values in a Dataframe, we need to first create one. We can do this using the pd.DataFrame() function, passing in a list or an array of data.

For example, let’s create a Dataframe of student grades. “`

import pandas as pd

data = {‘Name’: [‘John’, ‘Lisa’, ‘Mike’, ‘Sarah’],

‘Math’: [85, 95, 72, 90],

‘Science’: [93, 88, 65, 80],

‘English’: [88, 91, 70, 85]}

df = pd.DataFrame(data)

print(df)

“`

Output:

“`

Name Math Science English

0 John 85 93 88

1 Lisa 95 88 91

2 Mike 72 65 70

3 Sarah 90 80 85

“`

Using Python at() method to update the value of a row

The at() method in Pandas is used to access a single value in a Dataframe. We can use this method to update a single value in a row.

For example, let’s change Sarah’s Math grade to 95. “`

df.at[3, ‘Math’] = 95

print(df)

“`

Output:

“`

Name Math Science English

0 John 85 93 88

1 Lisa 95 88 91

2 Mike 72 65 70

3 Sarah 95 80 85

“`

Python loc() function to change the value of a row/column

The loc() function is more versatile than the at() method when it comes to accessing and modifying values in a Dataframe. We can use this function to update multiple values in a row or column at once.

For example, let’s update all of Mike’s grades to 80. “`

df.loc[df[‘Name’] == ‘Mike’, [‘Math’, ‘Science’, ‘English’]] = 80

print(df)

“`

Output:

“`

Name Math Science English

0 John 85 93 88

1 Lisa 95 88 91

2 Mike 80 80 80

3 Sarah 95 80 85

“`

Python replace() method to update values in a dataframe

The replace() method can be used to replace specific values in a Dataframe with new values. For example, let’s replace all instances of 80 with 85 in the Math column.

“`

df[‘Math’] = df[‘Math’].replace(80, 85)

print(df)

“`

Output:

“`

Name Math Science English

0 John 85 93 88

1 Lisa 95 88 91

2 Mike 85 80 80

3 Sarah 95 80 85

“`

Using iloc() method to update the value of a row

Finally, we can use the iloc() method to update a row by its integer index. For example, let’s update Lisa’s grades to 90 in one line of code.

“`

df.iloc[1, 1:] = 90

print(df)

“`

Output:

“`

Name Math Science English

0 John 85 93 88

1 Lisa 90 90 90

2 Mike 85 80 80

3 Sarah 95 80 85

“`

Conclusion

In this article, we have explored various techniques for updating row values in a Python Dataframe. We have demonstrated how to use the at(), loc(), and iloc() methods to modify specific values or ranges of values in a DataFrame, as well as how to use the replace() method to change all instances of a particular value.

By mastering these techniques, you will be able to efficiently manipulate and analyze your tabular data in Python with ease. To update row values in a Python Dataframe, there are various techniques available such as at(), loc(), iloc(), and replace() methods.

We can use the at() method to modify a single value in a row while loc() is used to modify multiple values in a row or column. The replace() method is used to change all instances of a particular value.

Finally, we can use the iloc() method to update a row using its integer index. With these techniques, we can efficiently manipulate and analyze our tabular data in Python with ease, emphasizing the importance of the topic.

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